发表机构
The Hong Kong University of Science and Technology (Guangzhou); Macquarie University; Ningxia University（香港科技大学（广州）; 麦考瑞大学; 宁夏大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对乘法伽马噪声在相干成像中的问题，提出γ-桥这一外观参数化扩散桥，通过特定调度连接噪声观测与干净极限，结合均匀补丁外观估计器，能处理多传感器数据，实现零样本恢复且结果领先，还提供可物理解释的控制。
AI 中文摘要
乘法伽马噪声是相干成像中与信号相关的退化，合成孔径雷达（SAR）去斑是其最突出的实际例子。现有扩散去噪器通过抽象的信噪比调度而非物理外观数L对正向过程进行参数化，不同部署场景通常需单独训练模型，且从合成伽马训练转移到真实SAR仍具挑战。我们引入γ-桥，其调度L(t)通过精确的乘法伽马边缘将L_obs处的噪声观测连接到干净极限。其闭式伽马-列维反向后验允许随机和确定性过程，观测条件和两步一致性损失稳定了低信噪比单视域中的多步推理。由于桥接时间直接代表L，一个条件网络可从任何允许的输入外观智能启动并在目标外观数处停止。这两个正交控制使得仅在具有合成伽马损坏的自然图像上以L_obs = 1进行训练后，能在整个允许网格上进行零样本恢复。结合均匀补丁外观估计器，γ-桥无需特定传感器微调即可处理来自六个星载和机载SAR传感器的数据，在标准合成基准上取得领先结果，同时提供了先前去噪器所没有的可物理解释的输入和输出控制。代码已发布。
英文摘要
Multiplicative Gamma noise is a signal-dependent degradation in coherent imaging; synthetic aperture radar (SAR) despeckling is its most prominent real-world instance. Existing diffusion denoisers parameterize their forward process by abstract signal-to-noise schedules rather than by the physical look number $L$, so different deployment scenarios typically require separately trained models, and transfer from synthetic Gamma training to real SAR remains challenging without clean ground truth. We introduce $γ$-Bridge, a look-parametric bridge whose schedule $L(t)$ connects the noisy observation at $L_{obs}$ to the clean limit through exact multiplicative Gamma marginals. Its closed-form Gamma--Lévy reverse posterior admits both stochastic and deterministic processes, while observation conditioning and a two-step consistency loss stabilize multi-step inference in the low-SNR single-look regime. Because bridge time directly represents $L$, one conditioned network can smart-start from any admissible input look and stop at a target look number. These two orthogonal controls enable zero-shot restoration over the full admissible grid after training only at $L_{obs} = 1$ on natural images with synthetic Gamma corruption. Combined with a homogeneous-patch look estimator, $γ$-Bridge processes data from six spaceborne and airborne SAR sensors without sensor-specific fine-tuning, achieving leading results on standard synthetic benchmarks while providing physically interpretable input and output controls absent from prior denoisers. Codes are released \href{https://github.com/Teriri1999/GammaBridge}{here}.
Comments13 pages, 9 figures
